Medical image registration is useful in many areas of medicine, for example radiosurgery. In radiosurgery, tumors and other lesions are treated by delivering a prescribed high dose of high-energy radiation to the target area, while minimizing radiation exposure to the surrounding tissue. Radiosurgery therefore calls for an ability to accurately focus on a target region, so that only the target receives the desired high doses of radiation, while surrounding critical structures are avoided. Typically, 3D imaging modalities, such as computed tomography (CT), magnetic resonance (MR) imaging, or positron emission therapy (PET) are used to generate diagnostic 3D images of the anatomical region containing the targeted area, for treatment planning purposes. These tools enable practitioners to identify the anatomical organs of a patient, and to precisely locate any abnormalities such as tumors.
To correct patient position or align radiation beam, the change in target position at the time of treatment (as compared to the position at the time of the diagnostic treatment planning) needs to be detected. This is accomplished by registering the 2D image acquired at the treatment time with the 3D CT scan obtained at the time of treatment planning.
The target positions are defined using the 3D diagnostic CT scan by physicians at the time of treatment planning. CT scans allow an image of the internal structure of a target object to be generated, one cross-sectional slice at a time. The CT data is used as the reference to determine the patient position change during treatment. Typically, synthesized 2D images such as digitally reconstructed radiographs (DRRs) are generated from the 3D CT data, and are used as 2D reference images. Similarity measures are used to compare the image intensity in the x-ray and the DRR images, in order to determine the patient pose change. In the field of medical image registration, this problem is categorized as a 2D/3D registration.
The methods used in the 2D/3D registration procedure can be divided into two categories. The first category includes methods based on image features. The image features may be anatomical edges, or segmented objects. Registration accuracy depends on the accuracy of edge detection, or the accuracy of object segmentation. The main advantage of feature-based methods is computation speed. Because the full information content of the image is not used, however, accuracy is sacrificed. The second category includes methods based on image intensity content. In intensity-based methods, the original images are used for the registration process. Therefore, a good accuracy can usually be achieved. Because a lengthy computation time is required, however, intensity-based methods are not practical for purposes of radiosurgery, or for clinical practice in general.
Image-guided radiosurgery requires precise and fast positioning of the target at the treatment time. In practice, the accuracy should be below 1 mm, and the computation time should be on the order of a few seconds. Unfortunately, it is difficult to meet both requirements simultaneously, because of several reasons. First, the two different modality images, i.e. CT scan images and x-ray images, have different spatial resolution and image quality. Generally, x-ray image resolution and quality are superior to the resolution and quality of DRR images. Second, DRR generation relies on a proper attenuation model. Because attenuation is proportional to the mass density of the target volume through which the beam passes through, the exact relationship between the traversed mass density and the CT image intensity needs to be known, in order to obtain an accurate modeling. Establishing this relationship is difficult, so a linear attenuation model is often used. However, the skeletal structures in DRR images cannot be reconstructed very well using the linear model, the DRRs being only synthetic x-ray projection images. Finally, x-ray images usually have a large image size (512×512). For better registration accuracy, it is desirable to use the full resolution image. Full resolution images are rarely used, however, due to the extremely slow computation that results from using such images.
U.S. Pat. No. 5,901,199 by Murphy et al. (the “Murphy patent”) describes a high-speed inter-modality image registration via iterative feature matching. The Murphy patent is a feature-based method. Prior to treatment, extraction and segmentation of silhouettes of the patient's skull are performed in order to make a feature mask. A set of DRR images are generated from the 3D CT data and are then masked, in order to isolate key pixels that are associated with anatomical edge features. The masked image contains only 5%–10% of the total image pixels. During treatment, the acquired x-ray images are similarly masked. The registration is conducted on the masked DRRs and the masked X-ray images. The registration process is completed in a few seconds. However, the accuracy and stability of the estimates are not sufficient to meet the sub-mm precision that is required in radiosurgery applications.
For these reasons, there is a need for a method and system for performing 2D/3D medical image registration using as little computing time as possible, while at the same time meeting the requisite accuracy for radiosurgical applications.